Algorithmic Learning Theory

9th International Conference, ALT’98, Otzenhausen, Germany, October 8–10, 1998 Proceedings

Häftad, Engelska, 1998

Av Michael M. Richter, Carl H. Smith, Rolf Wiehagen, Thomas Zeugmann

729 kr

Beställningsvara. Skickas inom 10-15 vardagar

Fri frakt för medlemmar vid köp för minst 249 kr.

This book constitutes the refereed proceedings of the 9th International Conference on Algorithmic Learning Theory, ALT'98, held in Otzenhausen, Germany, in October 1998. The 26 revised full papers presented were carefully reviewed and selected from a total of 34 submissions. Also included are three invited papers and an introduction by the volume editors. The papers are organized in sections on inductive logic programming and data mining, inductive inference, learning via queries, prediction algorithms, inductive logic programming, learning formal languages, and miscellaneous.

Produktinformation

Utgivningsdatum1998-09-23
Mått155 x 235 x 25 mm
Vikt692 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor444
FörlagSpringer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN9783540650133

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Artificiell intelligens inom Data och it
Programmeringsböcker inom Data och it

Editors’ Introduction.- Editors’ Introduction.- Inductive Logic Programming and Data Mining.- Scalability Issues in Inductive Logic Programming.- Inductive Inference.- Learning to Win Process-Control Games Watching Game-Masters.- Closedness Properties in EX-Identification of Recursive Functions.- Learning via Queries.- Lower Bounds for the Complexity of Learning Half-Spaces with Membership Queries.- Cryptographic Limitations on Parallelizing Membership and Equivalence Queries with Applications to Random Self-Reductions.- Learning Unary Output Two-Tape Automata from Multiplicity and Equivalence Queries.- Computational Aspects of Parallel Attribute-Efficient Learning.- PAC Learning from Positive Statistical Queries.- Prediction Algorithns.- Structured Weight-Based Prediction Algorithms.- Inductive Logic Programming.- Learning from Entailment of Logic Programs with Local Variables.- Logical Aspects of Several Bottom-Up Fittings.- Learnability of Translations from Positive Examples.- Analysis of Case-Based Representability of Boolean Functions by Monotone Theory.- Learning Formal Languages.- Locality, Reversibility, and Beyond: Learning Languages from Positive Data.- Synthesizing Learners Tolerating Computable Noisy Data.- Characteristic Sets for Unions of Regular Pattern Languages and Compactness.- Finding a One-Variable Pattern from Incomplete Data.- A Fast Algorithm for Discovering Optimal String Patterns in Large Text Databases.- Inductive Inference.- A Comparison of Identification Criteria for Inductive Inference of Recursive Real-Valued Functions.- Predictive Learning Models for Concept Drift.- Learning with Refutation.- Comparing the Power of Probabilistic Learning and Oracle Identification Under Monotonicity Constraints.- Learning Algebraic Structures from TextUsing Semantical Knowledge.- Inductive Logic Programming.- Lime: A System for Learning Relations.- Miscellaneous.- On the Sample Complexity for Neural Trees.- Learning Sub-classes of Monotone DNF on the Uniform Distribution.- Using Attribute Grammars for Description of Inductive Inference Search Space.- Towards the Validation of Inductive Learning Systems.- Consistent Polynomial Identification in the Limit.